A brutally honest story of building a full-stack app in the AI age - where every "firts commit" typo and late-night debugging session reveals what we're really signing up for
The Hook That Started Everything
It was August 20th, 2025, 11:47 PM. I typed git commit -m "firts commit" and hit enter.
Yes, "firts." With a typo. Because apparently, even in the age of AI coding assistants that can write entire applications, I still can't spell "first" correctly when I'm excited about a new project.
That typo-laden commit would become the first of 94 commits across 27 days - a journey that perfectly captures the paradox every developer faces in 2025: AI tools promise to make us faster and smarter, but somehow we're still debugging our own mistakes at 2 AM, wondering if we're more productive or just more confused.
The Numbers Don't Lie (But They Don't Tell the Whole Story Either)
Let me hit you with the raw data from my git archaeology:
- 94 commits in 27 days (that's 3.5 commits per day, for those keeping score)
- 100% solo development (just me, my coffee, and an army of AI assistants)
- Peak intensity: 78 commits in August alone
- Top commit message keywords: "api" (23 times), "implement" (21 times), "tests" (13 times)
But here's what those numbers don't show:
- Hours spent arguing with AI about why its "perfect" code wouldn't compile
- The number of times I copy-pasted AI suggestions that looked brilliant but were subtly, devastatingly wrong
- How many "implement frontend" commits were actually "please God just make this work" in disguise
Sound familiar? You're not alone.
The Great AI Coding Reality Check
While I was grinding through Job Pilot, researchers were documenting what every developer using AI tools secretly knows but rarely admits: we're not actually as productive as we think we are.
Recent studies show that 84% of developers now use AI coding assistants, but here's the kicker - experienced developers actually take 19% longer to complete tasks when using AI tools. We expected a 24% speedup. Instead, we got a productivity drag.
Why? Because AI solutions are "almost right, but not quite" 45% of the time. And debugging almost-right code is somehow more painful than writing it from scratch.
My commit history tells this exact story.
Act I: The Foundation Fantasy (August 20-25)
After that infamous "firts commit," I did what any developer does when starting fresh - I immediately restructured everything.
August 23rd: "Complete project restructure: app → backend"
This wasn't procrastination. This was wisdom. I was setting up proper separation of concerns before they became technical debt. The AI tools were great at generating boilerplate, but they had zero opinions about project architecture.
August 25th: The API explosion began.
In a single day, I built:
- Authentication & Authorization endpoints
- Job listings CRUD
- User profiles API
- Companies API
- Job Applications API
- Resumes API
My commit messages from that day read like a developer's fever dream:
- "feat: Implement FastAPI backend structure with TDD approach"
- "Add comprehensive job listings endpoints with validation"
- "Implement user authentication with JWT tokens"
I felt unstoppable. The AI was cranking out endpoint after endpoint. I was a full-stack architect, building the future of job searching.
Then I tried to connect it all together.
Act II: The API Renaissance Meets Reality (August 25-26)
August 26th was when I learned the first brutal lesson about AI-assisted development: AI is excellent at writing isolated components, terrible at making them work together.
While GitHub Copilot was suggesting perfect-looking API routes, and Claude was generating comprehensive test suites, none of them understood how my authentication middleware should interact with my database models, or why my job deduplication logic was creating infinite loops.
The commit messages tell the story:
- "Fix authentication middleware integration"
- "Debug job deduplication infinite loop"
- "Implement proper error handling across all endpoints"
This mirrors what 90% of developers report: AI tools struggle with large codebase context. They don't understand your existing patterns, your architectural decisions, or the subtle dependencies between your modules.
I was becoming a translator between different AI suggestions, spending more time debugging AI-generated integration bugs than I would have spent just writing the damn thing myself.
Act III: The Frontend Struggle is Real (August 27-30)
And then came the frontend. Oh, the frontend.
August 27th: "Write out plan for migrating jsx component to new reworked frontend service"
Translation: "The AI generated a bunch of React components that look perfect in isolation but form a Frankenstein's monster when assembled."
August 28th: "Restart on making the tsx rendering layer; using playwright mcp this time"
Translation: "Nothing works. Starting over. Again."
August 30th: The commits from this day perfectly capture the AI development experience:
- "Ai wrote a bunch for some reason" (When you let Claude take the wheel and it generated 200 lines of code you didn't ask for)
- "Done; no human validation, hope this went well" (Every developer's prayer when using AI)
- "Finish reworking frontend; still bugs" (Brutal honesty)
This is the reality nobody talks about: AI tools excel at the easy stuff but struggle with the integration layer where real applications live.
Act IV: The Testing Enlightenment (Late August)
Here's where my story diverges from the typical "AI made me super productive" narrative. When everything was falling apart, I doubled down on testing.
Not because I'm some testing evangelist, but because testing was the only way to verify that AI suggestions actually worked.
My commits became:
- "Big effort to implement playwright testing"
- "Implement full-stack test suite, and first integration tests"
- "Create foundation for Playwright Testing"
Testing became my AI reality check. When Claude confidently assured me that its authentication flow was "production-ready," my tests caught the security holes. When Copilot generated database queries that looked elegant, my integration tests revealed they'd break under load.
This is the pattern successful AI-assisted developers follow: Use AI for generation, humans for validation, and tests for truth.
Act V: The September Reset (The Plot Twist)
After August's 78-commit marathon, something interesting happened. I burned out. Hard.
But instead of abandoning the project, I did something that separates experienced developers from beginners: I made a strategic reset.
September 15th: "Branch reset: Reset main branch to ff06fcd" followed by "Restart Frontend Components"
This wasn't failure. This was wisdom. Sometimes the best progress is admitting your current approach isn't working and starting fresh with better knowledge.
The September commits show a more measured approach:
- "Remove a bunch of the bloat"
- "Begin frontend rework" (yes, another typo - some things never change)
- "Create first draft of frontend rewrite"
The Real Lessons (What They Don't Tell You About AI Development)
After 94 commits and 27 days, here's what I learned about developing with AI in 2025:
1. AI is a Powerful Intern, Not a Senior Developer
AI tools are incredible at:
- Generating boilerplate code
- Writing isolated functions
- Creating comprehensive test cases
- Suggesting patterns you forgot existed
But they're terrible at:
- Understanding your existing codebase context
- Making architectural decisions
- Debugging integration issues
- Knowing when to stop generating code
2. The "Almost Right" Problem is Real
That 45% statistic about AI being "almost right, but not quite"? It's devastatingly accurate.
Almost-right code is worse than obviously broken code because it looks correct until it fails in production. You spend more time debugging subtle AI mistakes than you would writing correct code from scratch.
3. Testing Becomes Non-Negotiable
In the pre-AI era, you could sometimes get away with light testing if you wrote careful code. With AI assistance, comprehensive testing isn't optional - it's the only way to verify that generated code actually works.
4. Humans Excel at Integration and Architecture
AI generates components. Humans integrate systems. The real skill in AI-assisted development isn't prompting the AI to write perfect code - it's knowing how to combine AI-generated pieces into a coherent, maintainable system.
5. The Restart is a Feature, Not a Bug
Traditional development wisdom says "never rewrite." AI-assisted development changes this. Sometimes, starting fresh with better prompts and clearer architecture is faster than debugging a messy AI-generated codebase.
The Paradox We're All Living
Here's the thing that nobody wants to admit: AI coding tools are simultaneously the best and most frustrating thing to happen to development.
They're the best because they can generate complex, sophisticated code in seconds. They handle boilerplate, suggest patterns, and can kickstart projects that would take days to set up manually.
They're the most frustrating because they create a false sense of progress. You feel incredibly productive generating hundreds of lines of code, until you realize none of it works together and you're debugging problems you don't understand.
What This Means for You
If you're using AI coding tools (and statistically, you probably are), here's my advice based on 94 commits of real experience:
Start with Architecture, Not Code
Don't let AI drive your technical decisions. Plan your system architecture first, then use AI to implement the pieces.
Embrace the Human-AI Workflow
Use AI for generation, yourself for validation, and tests for verification. This trinity is your safeguard against the "almost right" problem.
Budget for Integration Time
AI can generate components in minutes, but integrating them takes human time. Plan accordingly.
Test Everything
If AI wrote it, test it. If you modified AI code, test it again. If it looks too good to be true, definitely test it.
Know When to Reset
Sometimes starting fresh with better prompts is faster than debugging a tangled AI-generated mess.
The Job Pilot Epilogue
As I write this, Job Pilot is alive and actively being developed. It's not the perfect app I envisioned on August 20th, but it's something better - a real application built through the messy, iterative process of human-AI collaboration.
The final commit message as of September 16th reads: "Document frontend-backend connection"
Not "Revolutionary AI-Generated App Launches" or "Perfect Code Generated by AI." Just the humble work of documenting how pieces fit together - the most human part of development.
That's the real story of coding with AI in 2025. It's not about AI replacing developers or making us obsolete. It's about learning to collaborate with incredibly powerful but fundamentally limited tools.
We're not just writing code anymore. We're conducting an orchestra where half the musicians are brilliant but can't read music, and the other half (us) need to make sure everyone plays in harmony.
And sometimes, just sometimes, when all the pieces align and the tests pass and the user clicks through your app without encountering a single bug, you remember why you fell in love with building software in the first place.
Even if your first commit had a typo.
What's Your AI Development Story?
I've shared my 94-commit journey into the reality of AI-assisted development. Now I want to hear yours.
Have you experienced the "almost right, but not quite" problem? How do you balance AI assistance with human judgment? What's your biggest AI development win or failure?
Share your story in the comments - let's build a real picture of what development looks like in the AI age, beyond the hype and the headlines.
Want to see the complete commit history that inspired this post? Check out the Job Pilot repository (coming soon) or follow my development journey for more real stories from the trenches of AI-assisted coding.
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